Instead, in our decentralized tracking framework, each camera node performs feature extraction and obtains likelihood functions.
This paper was the first to describe the technique known as lasso (or L1 regularization), which performs feature selection in conjunction with learning.
The algorithm performs feature extraction, feature matching, and finally, feature depth computation.
The front end of the framework performs feature extraction to derive samples.
Each stage of a conventional cascade performs feature (weak classifier) selection.
The front end of the framework performs feature extraction from the audio signals of user conversation.
Embedded approach [9] performs feature selection during the process of training and is specific to the applied learning algorithms.
In fact, L1 regularized logistic regression model inherently performs feature selection.
This workflow performs feature detection, RT correction, gap filling, feature annotation, in silico fragmentation, and spectral matching to databases.
But unlike the Lasso, which performs feature selection through penalization (soft thresholding), SNSS does it through hard thresholding.
